Getting nan for loss since first iteration

[Kumar Ayush]

I have about a million data points, and I cleaned it as much as I could, and it doesn't make sense to me why should I get my objective value as nan.

Here is what my input looks like

>>> X
array([[ 21.82658,  19.2083 ,  17.83528,  16.52586,  15.84394],
       [ 21.01001,  19.10402,  18.2206 ,  18.00173,  17.80133],
       [ 22.62348,  19.84317,  18.5044 ,  17.89358,  17.57959],
       ...,
       [ 21.74205,  20.28558,  20.04322,  19.96841,  20.10308],
       [ 21.20579,  20.28571,  19.65784,  19.627  ,  19.43284],
       [ 23.14987,  21.31635,  21.09137,  21.09221,  20.83237]])

And my output

>>> Y
array([[  4.49888400e-06],
       [ -1.54541000e-04],
       [  1.11225300e-05],
       ...,
       [ -3.71117900e-05],
       [  1.02806300e-04],
       [  3.01237300e-04]])

And here is how I build my model

from keras.models import Sequential
from keras.layers import Dense, Activation

model = Sequential()
model.add(Dense(10,input_dim=5))
model.add(Activation('sigmoid'))
model.add(Dense(1,activation='sigmoid'))

#model.compile(optimizer='rmsprop', loss='mse')
model.compile(optimizer='rmsprop', loss = 'msle')

model.fit(X,Y,nb_epoch=5,batch_size=100)

And here is the output I get

Epoch 1/5
1029120/1029120 [==============================] - 10s - loss: nan      
Epoch 2/5
1029120/1029120 [==============================] - 10s - loss: nan      
Epoch 3/5
1029120/1029120 [==============================] - 10s - loss: nan      
Epoch 4/5
1029120/1029120 [==============================] - 10s - loss: nan      
Epoch 5/5
1029120/1029120 [==============================] - 10s - loss: nan      
<keras.callbacks.History object at 0x7f7ec9046ad0>

nan all the way. Is there anyway I can debug this?

I tried changing my activation functions to RELU and SoftMax, but that doesn't simplify anything. I thought maybe mse for such numbers is going out of range, but msle also fails. I am not asking for why is this happening, although that will be helpful. But how do I look deeper into what's going on in order to figure out what's up with this?

[Daπid]

Your targets look tiny, you should normalise them (0-1 range, log transform, mean 0 and std 1...). Your features are all around 20, they could benefit from the same treatment.

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[Matias Valdenegro]

I believe this is happening because you are not normalizing your input and
output values. Normalize them so they lie between 0 and 1 and it should start
training successfully.

> *Is there anyway I can debug this?*I tried changing my activation functions

> to RELU and SoftMax, but that doesn't simplify anything. I thought maybe

> *mse* for such numbers is going out of range, but *msle *also fails. I am